Structural Dominance Analysis
of Large and Stochastic Models
Rogelio Oliva
Mays Business School
Wehner 301F- TAMU 4217; College Station, TX 77843
Phone 979-862-3744; Fax 979-845-5653
roliva@tamu.edu
Oliva R. 2016.
Structural Dominance Analysis of Large and Stochastic Models.
System Dynamics Review 32(1):25-51.
DOI:10.1002/sdr.1549.
Abstract
The last decade and a half has seen significant efforts to develop and automate methods for identifying structural dominance in system dynamics models. To date, however, the interpretation and testing of these methods has been with small deterministic models (less than five stocks) that show smooth behavioral transitions. While the analysis of simple and stable models is an obvious first step in providing proof of concept, the methods have become stable enough to be tested on a wider range of models. In this paper I report the findings from expanding the application domain of these methods in two important dimensions: increasing model size and incorporating stochastic variance in some model variables. I find that the methods work as predicted with large stochastic models, that they generate insights that are consistent with the existing explanations for the behavior of the tested model, and that they do so in an efficient way.
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Last updated September 28, 2017.